The full integration of all countries into global markets is a hoped-for outcome, not a reality.
Market frictions and illiquidity premia
The impact of supply–demand factors—and of investor irrationalities—on asset prices is made possible by market frictions. Frictions related to illiquidity, funding constraints, and trading costs, as well as counterparty risk, agency concerns, and other information problems can be first-order important, as the 2008 experience shows. Bearish expectations, elevated risk, and risk aversion do not alone explain the distressed price levels of securitized bonds and other assets. Many financial intermediaries and investors became forced sellers as market frictions prevented them and other investors from taking advantage of good deals or nearly riskless arbitrage opportunities.

…

Opportunities that appeared compelling over the long horizon could not be taken due to the possibility that further de-levering and related mark-to-market volatility would make the investment positions unsustainable over the short run.
A diverse literature on market frictions explains why asset prices might deviate from fair values or respond sluggishly to new information. However, few asset-pricing models relate asset risk premia to market frictions such as funding and liquidity constraints. Garleanu–Pedersen (2009a) propose a model in which the CAPM risk premium is boosted when binding funding constraints make capital scarce, while Acharya–Pedersen (2005) have adjusted the CAPM to include liquidity-related premia.
Illiquidity is the most important market friction. Early studies of liquidity premia focused on the cost (rather than the risk) of illiquidity—they observed that greater trading costs when buying and selling assets would reduce expected net returns.

…

Peso problems and learning stories help in interpreting past return predictability but contain no lessons about future profit opportunities.
Market frictions
Most academic predictability evidence is presented without taking into account trading costs and other market frictions. It is not surprising, then, that paper profits tend to be most consistent in illiquid assets (e.g., small-cap stocks) or in trading styles that involve high turnover (e.g., short-term reversal). Faced with evidence of profitable trading strategies, it is always reasonable to question whether trading cost estimates (including both direct costs and market impact) have been understated. Fortunately, newer studies increasingly adjust profits for trading costs, financing costs, short-selling constraints, and other market frictions. While the limits-to-arbitrage literature explains why speculative capital is generally scarce, these adjustments explain why certain paper regularities are harder to exploit in practice than others.

Airbnb is an epic leap forward when compared to the epic leap of faith involved in renting a room via its predecessors, the classified ads or Craigslist.
But let’s not confuse a set of groundbreaking market innovations with the end of market frictions. Yes, there are entire websites devoted to Airbnb horror stories—the trashed homes, the tenant-turned-squatter. There’s an equal number of angry rants directed at Uber. Neither of us rents our idle real estate assets when we’re out of town and not because we’re old-fashioned. We’ve also experienced market frictions of a more mundane variety. In writing this book we went to Washington to interview George Akerlof of market-for-lemons fame.
As a bit of add-on market research, one of us, Ray, decided to rent an apartment for the night via Airbnb. The renter’s credentials were impeccable.

…

But to get your $60 billion valuation, you need to create as many frictions as possible for everyone else. Although proponents of the sharing economy tout its ability to reduce market frictions, the only way they’re going to make the kinds of profits they (and their investors) want is to create new ones. That’s something they’re not interested in talking about to the public at large, or to their representatives in government.
This leaves a bit of a paradox in the techno-utopian free-market narrative. A great entrepreneur will use technology to create a fantastic new market, then will use technology to set up market frictions to protect it. As entrepreneur and venture capitalist Peter Thiel wrote in the Wall Street Journal, “Competition Is for Losers.”12
Don’t get us wrong. We’re not faulting the market makers of Silicon Valley nor begrudging them for the profits they’ve generated and captured.

…

We need to figure out how to adapt our models to reality, not the other way around.
Sharing
Economists aren’t the only ones trying to recast the world in our model’s image. If friction—informational, transactional, contractual—is all that stands between textbook economic models and the functioning of our real economy, then there is a vocal contingent out there (“there” being mostly Silicon Valley) that sees technology as the solution.
When viewed through the lens of market frictions, the much-hyped notion of the sharing economy can be seen as an effort to bring free-market salvation to bricks, mortars, and automobiles.
If you’ve ever tried to hail a taxi in San Francisco or rent a room in Washington, DC, you know the frictions of which we speak. The Bay Area’s sprawl, combined with strict regulations on the cab and livery businesses, used to leave you at the mercy of the two thousand or so taxi medallion holders that covered San Francisco’s 230 square miles.

If a country fails to invest enough, its capital stock decreases as it depreciates and becomes obsolete. One driver of investment is how low the real interest rate is, which depends in part on the inflation risk premium
(i.e., stable inflation is best) and the rule of law. Also, supply shocks can arise from changes in labor-market frictions (sticky wages, search frictions, and rigid labor laws), product-market frictions (sticky prices and anticompetitive corporate measures), and capital-market frictions (market and funding illiquidity) leading to unemployment and lower capital utilization. For instance, a systemic banking crisis slows growth because the ability to finance projects is a driver of investment. In the long run, output depends on supply factors such as technological progress and population growth.
11.4.

…

., salaries to traders, computers, rent, legal fees, and auditors). Investors are willing to bear these costs and fees when they are outweighed by the profits that the manager is expected to extract from the efficiently inefficient market.
How close are prices and returns to their fully efficient values in an efficiently inefficient market? Well, because of competition, securities’ returns net of all the relevant market frictions—transaction costs, liquidity risk, and funding costs—are very close to their fully efficient levels in the sense that consistently beating the market is extremely difficult. However, despite returns being nearly efficient, prices can deviate substantially from the present value of future cash flows. To understand this apparent paradox, note that the return to buying a cheap stock, say, depends both on the price today and the price tomorrow.

That’s the sort of problem that an important, but until recently overlooked, type of business sets out to solve by helping parties who have something valuable to exchange find each other, get together, and do a deal.
Multisided Platforms
In 1998, this important type of business didn’t have a name. That’s surprising, in retrospect. Many businesses had been built to reduce these sorts of market frictions, which economists tend to call transaction costs. Their basic business model had been around for thousands of years. But business schools didn’t teach classes on how to start or run businesses that help different parties get together to exchange value. Economists didn’t have a clue how these businesses worked. In fact, the companies that reduced these market frictions charged prices and adopted other strategies that economic textbooks insisted no sensible business would do.
Now we call these businesses multisided platforms.2 Don’t let the economists’ unsexy name fool you.3 Multisided platforms are anything but boring.

…

The bigger the friction, the greater the value the platform can potentially provide, the greater the opportunity for getting participants on board, and the greater the chance for the platform to make money. Knowing which type of participants benefits the most from eliminating that friction can guide decisions on ignition strategies as well as on pricing.
Sometimes, as with OpenTable, the platform drastically reduces a clear market friction, and the issue is whether the friction is big enough to enable the platform to earn adequate revenue to cover all the costs of launching and running the platform. In other cases, the platform pioneer has identified a new way for participants to interact—one that no one recognized because there was no way to do it. People and restaurants were already making and taking reservations before OpenTable came along.

…

Less than ten years later, in 2014, more than 84 percent of Kenyan mobile phone users, including many of the very poor, were able to use their mobile phones to transfer money to each other, to pay their bills, and to pay at stores.7 People can now also use new financial services available through their mobile money accounts to save money and take out loans, and many do.8 Increasingly, stores are accepting mobile money for payment.
The way this happened in Kenya is a remarkable story of how a company figured out how to ignite a multisided platform in trying circumstances, to massively reduce important market frictions, and to provide financial services to millions of impoverished people. And it is a story of how multisided platforms—M-PESA and other mobile money schemes that have started in Kenya and elsewhere—are leapfrogging traditional industries. Kenyans don’t need to rely on banks for many financial services. And while it is too soon to tell, Kenyan merchants and consumers may end up using mobile money instead of traditional payment cards and point-of-sale equipment.

It was a lot of fun. No data, no computer
even—just pen and paper. I remember making ample use of hyperbolic
tangent functions to represent the value of information without having
to worry about my estimates of expected returns getting too far above
the bid or below the offer.
Modeling stocks in a microstructure framework, for the purpose of
earning a profit, is all about modeling market frictions. Bid/offer spreads
are a market friction; so is the fact that markets have to open and close
and that you cannot trade in unlimited size. This was hardly sexy stuff in
the early 1990s, when all the rage was customized curvature in the form
of structured derivative products or over-the-counter options. At that
time, quantitative finance was all about improving upon Black-Scholes.
I loved being apart from the crowd.

…

Everything I had seen in the finance literature
up to this point searched for market anomalies using closing prices or
assumed continuous, single-price processes. And yet, this was not the
way the world worked, I thought. Stocks trade in a double-auction
framework. A trade results from someone hitting a bid, taking an offer,
or two sides agreeing in the middle. So looking at how stocks moved
short-term meant studying market frictions and the discreteness of how
stocks moved from bid/offer to bid/offer. As far as I could tell, no one
had studied this before. There was no box; so thinking out of it was all
one could do. It was then I figured out how I wanted to make my mark.
JWPR007-Lindsey
April 30, 2007
16:14
Andrew J. Sterge
325
The story of APS seems to me a neat lesson in how quantitative
finance can evolve from humble roots.

Ronald Coase complained that the market has a “shadowy role” in economic theory, and “discussion of the market itself has entirely disappeared.”
The Nobel laureates’ critique has now been addressed. Modern economics has a lot to say about the workings of markets. Theorists have opened up the black box of supply and demand and peered inside. Game theory has been brought to bear on the processes of exchange. Examining markets up close, the new economics emphasizes market frictions and how they are kept in check. In 2001, this work received recognition with the award of the Nobel Prize in economics to George Akerlof, Michael Spence, and Joseph Stiglitz for laying the foundation, as the Nobel citation said, “for a general theory of markets with asymmetric information.” Expressed in mathematics and impenetrable jargon, these new ideas reside obscurely in the technical journals.

…

Because of the buyers’ cost of searching, the merchants make a large profit. Big effects can come from small transaction costs.
Today’s economics has the problem of information at its core. The “biggest new concept in economics in the last thirty years,” Kenneth Arrow said in 2000, “is the development of the importance of information, along with the dispersion of information.”4
Two kinds of market frictions arise from the uneven supply of information. There are search costs: the time, effort, and money spent learning what is available where for how much. And there are evaluation costs, arising from the difficulties buyers have in assessing quality. A successful market has mechanisms that hold down the costs of transacting that come from the dispersion of information.
Search costs can cause markets to malfunction in large and small ways.

…

Firms that contract out some of their production—buying rather than making—place their trust in the market mechanism.
If markets achieve such impressive efficiencies, why are so many transactions deliberately taken out of the market and put into the planned sub-economies that are firms? Why isn’t everyone an independent contractor instead of a hired employee? The answer is that firms exist as a response to market frictions. Sometimes it is less expensive to run a hierarchy than to use the market. Whether a firm produces its inputs in-house or procures them from other firms depends on the relative costs of each form of transaction. One of the factors affecting this comparison, as Ronald Coase wrote in 1937, is the efficiency with which markets work. Where the transaction costs of using the market are high, firms tend to make inputs themselves.

Tough market
conditions, an unexpected change in regulation, or terrorist events can
destroy credible public companies overnight.
r Transaction costs may wipe out all the profitability of stat-arb trading,
particularly for investors deploying high leverage or limited capital.
r The bid-ask spread may be wide enough to cancel any gains obtained
from the strategy.
r Finally, the pair’s performance may be determined by the sizes of the
chosen stocks along with other market frictions—for example, price
jumps in response to earnings announcements.
Careful measurement and management of risks, however, can deliver
high stat-arb profitability. Gatev, Goetzmann, and Rouwenhorst (2006) document that the out-of-sample back tests conducted on the daily equity data
from 1967 to 1997 using their stat-arb strategy delivered Sharpe ratios well
in excess of 4. High-frequency stat-arb delivers even higher performance
numbers.

The potential gains, particularly to early entrants, are so great that it would be worth incurring extremely heavy administrative costs.10
Whatever the reason, an imperfection of the market has led to underinvestment in human capital. Government intervention might therefore be rationalized on grounds both of “technical monopoly,” insofar as the obstacle to the development of such investment has been administrative costs, and of improving the operation of the market, insofar as it has been simply market frictions and rigidities.
If government does intervene, how should it do so? One obvious form of intervention, and the only form that has so far been taken, is outright government subsidy of vocational or professional schooling financed out of general revenues. This form seems clearly inappropriate. Investment should be carried to the point at which the extra return repays the investment and yields the market rate of interest on it.

At this point, the dealer revises
her quotes to $109 bid, offered at $111 (thus bracketing the new value).
Each of the other 99 customers will perceive an opportunity cost of $10
(=$110 − $100) and may well attribute this to sloth on the part of their brokers or their systems. Thus, the aggregate opportunity cost is $990, for an
aggregate implementation shortfall of $991. It is nonsensical, of course, to
suggest that aggregate welfare could be enhanced by this amount if market
frictions or broker ineptitude were eliminated.
The problem is that the benchmark price of π0 = $100 does not come
close, given the new information, to clearing the market. The profits realized by the lucky first trader are akin to lottery winnings. Individual
traders might attempt to gain advantage by increasing the speed of their
order submission linkages, but because only one trader can arrive first,
the situation is fundamentally a tournament (in the economic sense).
14.2.1 The Implementation Cost for
Liquidity Suppliers
Is implementation cost a useful criterion for liquidity suppliers?

First, it is not clear that those mispricings actually occur, given the
noise in estimates. Second, trading strategies are not easily amenable to long holding periods.
Short-term price patterns considered here are of two kinds: the
pattern could be a form of price drift, where the price continues to
move in the same direction, or it could be a price reversal where the
56
Short-Term Price Drift
price moves in the opposite direction. These price patterns may be
due to market frictions, a result of market inefficiency, or related to
information arrival.
The earliest documentation of short-term price drift is related to
earnings announcements, in which it was found that firms with surprisingly good earnings earn abnormal returns of about 2 percent
in the following three months, whereas firms with surprisingly bad
earnings lose abnormally. Evidence of price drift suggests that information in the earnings announcement is not immediately and
fully reflected in prices.

Now in place
of (I.3.141) we write
dXt = − Xtdt + dBt
(I.3.142)
The parameter is called the rate of mean reversion and the parameter is called the long
term value of X. In Section II.5.3.7 we prove that the discrete time version of (I.3.142) is a
stationary AR(1) model.
I.3.7.3
Stochastic Models for Asset Prices and Returns
Time series of asset prices behave quite differently from time series of returns. In efficient
markets a time series of prices or log prices will follow a random walk. More generally, even
in the presence of market frictions and inefficiencies, prices and log prices of tradable assets
are integrated stochastic processes. These are fundamentally different from the associated
returns, which are generated by stationary stochastic processes.
Figures I.3.28 and I.3.29 illustrate the fact that prices and returns are generated by very
different types of stochastic process. Figure I.3.28 shows time series of daily prices (lefthand scale) and log prices (right-hand scale) of the Dow Jones Industrial Average (DJIA)
DJIA
12000
9.4
Log DJIA
9.3
11000
9.2
10000
9.1
9000
9
8000
8.9
Sep-01
May-01
Jan-01
Sep-00
May-00
Jan-00
Sep-99
May-99
Jan-99
Sep-98
May-98
8.8
Jan-98
7000
Figure I.3.28 Daily prices and log prices of DJIA index
56
This is not the only possible discretization of a continuous increment.

Since the start of the euro, it continues as what is sometimes called ERM II, a band linking the Danish krone to the euro.
37 Though the basic idea of full employment is clear—that everyone who would like a job can get one at the prevailing wages for those with the individual’s skills and talents—there is some controversy over the precise definition of full employment. The general notion is that the labor market is just sufficiently loose—with job seekers matching employers looking for employees—that there is no inflationary pressure. Because of labor market frictions—it takes time to find a good match between employers and employees—this “natural rate” is greater than zero, normally around 2 to 3 percent. Unemployment might also exist because of rigidities in the adjustment of relative wages—the labor market for skilled labor might be so tight that wages are rising, but there may still be unemployment of unskilled workers. This level of unemployment is sometimes referred to as structural unemployment.

But some managers
and academics claim that the lower price should make the stock more attractive for capital-constrained investors, thereby increasing demand, improving
liquidity, and leading to higher returns for shareholders.41
In many cases, a stock split is indeed accompanied by positive abnormal
returns to shareholders (see Exhibit 5.16).42 The abnormal returns have nothing to do with the split as such but are simply a function of self-selection and
signaling. Self-selection is the tendency of companies to split their stocks into
lower denominations because of a prolonged rise in their share price, as shown
in Exhibit 5.16. As a result, one should expect any sample of companies that
40 R.
D. Boehme and B. R. Danielsen report over 6,000 stock splits between 1950 and 2000: “Stock-Split
Post-Announcement Returns: Underreaction or Market Friction?” Financial Review 42 (2007): 485–506.
D. Ikenberry and S. Ramnath report over 3,000 stock splits between 1988 and 1998: “Underreaction to
Self-Selected News Events: The Case of Stock Splits,” Review of Financial Studies 15 (2002): 489–526.
41 There is ample evidence to show that this is not the case: after a split, trading volumes typically
decline, and brokerage fees and bid-ask spreads increase, indicating lower liquidity, if anything.